Displaying 1 - 10 of 25 results

Traditional metrics for live migration of Virtual Machines (VM) include total migration time, downtime, network overhead, and application degradation. In this paper, the authors introduce a new metric, eviction time, defined as the time to evict the entire state of a VM from the source host. Eviction time determines how
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MapReduce has become a popular framework for big data applications. While MapReduce has received much praise for its scalability and efficiency, it has not been thoroughly evaluated for power consumption. In this paper, the authors explore the possibility of scheduling in a power-efficient manner without the need for expensive power
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MapReduce has gradually become the framework of choice for \"Big data\". The MapReduce model allows for efficient and swift processing of large scale data with a cluster of compute nodes. However, the efficiency here comes at a price. The performance of widely used MapReduce implementations such as Hadoop suffers in
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Since its inception, MapReduce has frequently been associated with Hadoop and large-scale datasets. Its deployment at Amazon in the cloud, and its applications at Yahoo! and Facebook for large-scale distributed document indexing and database building, among other tasks, have thrust MapReduce to the forefront of the data processing application domain.
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Multi-tenancy is one of the most attractive features of cloud computing, which provides significant benefits to both clients and service providers by supporting elastic, efficient, and on-demand resource provisioning and allocation. However, this architecture also introduces additional security implications. Client Virtual Machine (VM) instances running on the same physical machine
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Gang migration refers to the simultaneous live migration of multiple Virtual Machines (VMs) from one set of physical machines to another in response to events such as load spikes and imminent failures. Gang migration generates a large volume of network traffic and can overload the core network links and switches
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In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a main focus area for \"Big data\" research.
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Shrew Distributed Denial-of-Service (DDoS) attacks are stealthy, concealing their malicious activities in normal traffic. Although it is difficult to detect shrew DDoS attacks in the time domain, the existent energy exposes them in frequency domain. For this purpose, online Power Spectral Density (PSD) analysis necessitates real-time PSD data conversion. In
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Wireless Sensor Networks (WSNs) are vulnerable to security attacks due to the broadcast nature of transmission and limited computation capability. After Intrusion Detection Systems (IDSs) identifies an mobile intruder, IDS may broadcast the blacklist to all nodes in network. This method is energy inefficient because all nodes have to receive
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Data Stream Management Systems (DSMS) aim to process massive data streams in a timely fashion to support important applications, e.g., financial market analysis. However, DSMS can be overloaded due to large bursts in data stream arrivals and data-dependent query executions. To avoid overloads, the authors design a new load shedding
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